most citedLiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking

2 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Interactive Tracking: A Human-in-the-Loop Paradigm with Memory-Augmented Adaptation

Yuqing Huang, Guotian Zeng, Zhenqiao Yuan +4

Existing visual trackers mainly operate in a non-interactive, fire-and-forget manner, making them impractical for real-world scenarios that require human-in-the-loop adaptation. To…

cs.CV2023★ 1 cited

RTrack: Accelerating Convergence for Visual Object Tracking via Pseudo-Boxes Exploration

Guotian Zeng, Bi Zeng, Hong Zhang +2

Single object tracking (SOT) heavily relies on the representation of the target object as a bounding box. However, due to the potential deformation and rotation experienced by the…

cs.CV2023★ 2 cited

LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking

Qingmao Wei, Bi Zeng, Jianqi Liu +2

The recent advancements in transformer-based visual trackers have led to significant progress, attributed to their strong modeling capabilities. However, as performance improves, r…

cs.CV2023★ 1 cited

Towards Efficient Training with Negative Samples in Visual Tracking

Qingmao Wei, Bi Zeng, Guotian Zeng

Current state-of-the-art (SOTA) methods in visual object tracking often require extensive computational resources and vast amounts of training data, leading to a risk of overfittin…

cs.CV2023★ 2 cited

Efficient Training for Visual Tracking with Deformable Transformer

Qingmao Wei, Guotian Zeng, Bi Zeng

Recent Transformer-based visual tracking models have showcased superior performance. Nevertheless, prior works have been resource-intensive, requiring prolonged GPU training hours…